How to make your data FAIR through Data Standardization
Regulatory demands and the need for compliance often drive data standardization. However, it also serves as a crucial element in enabling usage of advanced technologies and supporting Life Science companies in fully realizing the benefits of undergoing digital transformation.
The main driver behind data standardization is often external and caused by regulatory demands specifically around the data submitted to the Health Authorities (e.g. CDISC, IDMP, eCTD, etc.). The need for data standardization is however not restricted to only involve adoption of industry data standards and to focus on the need to stay compliant. Data standardization also serves as a strategic component in adopting to the changing environment within the Life Science industry which is becoming increasingly data-driven.
A key goal of data standardization is to ensure that machines and humans can use the generated data. To ensure this, the guiding principles to use is FAIR, meaning data is Findable, Accessible, Interoperable and Reusable. Having FAIR data is a crucial prerequisite for increasing operational efficiency through digitalization. It is ultimately essential to gain more value out of the data.
Reaching the point where systems, processes, and people in the organization agree on a set of data standards can pose a significant challenge. Not only does it require the IT systems to “speak the same language” thereby making data transferable. It also needs an agreed and clear definition of what a given data standard means within an organization along with a transparent and efficient data governance model that ensures proper maintenance on the operational level.
Data standardization projects should include a thorough analysis of the current processes which generate and use the impacted data. This enables identification of pain points and opportunities. It serves as a basis to detect the data flow gaps and define the future processes. Involving all departments that utilize processes affected by data standards is essential. To avoid the scope becoming too big and involving too many stakeholders, a staggered approach may be beneficial. This may be helpful when dealing with extensive data mapping and large migration tasks as a result of implementation of data standards.
Organizations may initially face resistance when adopting data standards. This is due to the lack of flexibility experienced when moving from an uncontrolled way of working. It is critical not to underestimate the importance of organizational change management. This is to support the impacted stakeholders in adopting the change and recognizing the value. Training in using the standards and showing the benefits downstream in the processes assures support by the organization. Anchoring the data governance high in the organization help enforce the standards.
Data standardization as a prerequisite for advanced technologies
Data standardization results in one of the true game changers—the potential for using more advanced technologies as part of the digital transformation within the Life Science industry.
- Potential for automation – The same data is often manually replicated into multiple sources. Identification of a source of truth and a system of record for a given data standard supported by the right infrastructure will allow the use of the data for automatic reuse by feeding into various downstream systems, thereby reducing time and cost.
- Usage of advanced analytics – Tools employing artificial intelligence, machine learning, and data mining hold great potential for extracting more value from existing data. They will transform how data is managed, empowering innovation and enhancing operational efficiency.
Without good data quality in place, none of these benefits can be realized. Data standardization is an essential piece in laying the foundations for enabling the usage of advanced technologies. It will provide a competitive advantage for Life Science companies.
Do you need help with data standardization?
At HERAX we support clients with implementation of data standards for the Life Science industry. We also help with internal data standardization projects (e.g. eProtocols, CDR, CDW, SCE, eCRFs, CDISC etc.). We can help you create standards strategies, optimize your processes and implement solutions required in order to support the data standards.
Cecilie supports clients in the pharmaceutical industry and has participated in several projects across Europe and in Japan. Specializing in process optimization and the implementation of IT solutions, she holds project experience across all main areas of R&D, from Early Research to Regulatory Affairs. During the last three years, she has gained extensive experience with EDMS implementation, Data Standardization, the Clinical Trial Regulation and Laboratory Data Management.
Cecilie has a strong background in biomedical research and holds a M.Sc. in Molecular Biomedicine.